Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations3774
Missing cells7199
Missing cells (%)8.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 MiB
Average record size in memory621.8 B

Variable types

Categorical10
Text4
Numeric9

Alerts

bathroom is highly overall correlated with bedRoom and 4 other fieldsHigh correlation
bedRoom is highly overall correlated with bathroom and 4 other fieldsHigh correlation
builtup is highly overall correlated with carpet_area and 2 other fieldsHigh correlation
carpet_area is highly overall correlated with bathroom and 4 other fieldsHigh correlation
facing is highly overall correlated with builtupHigh correlation
price_in_lacs is highly overall correlated with bathroom and 5 other fieldsHigh correlation
price_per_sqft_inrs is highly overall correlated with price_in_lacsHigh correlation
property_type is highly overall correlated with bedRoom and 2 other fieldsHigh correlation
servant_room is highly overall correlated with bathroom and 1 other fieldsHigh correlation
super_builtup is highly overall correlated with bathroom and 6 other fieldsHigh correlation
store_room is highly imbalanced (56.3%)Imbalance
super_builtup has 1861 (49.3%) missing valuesMissing
builtup has 2072 (54.9%) missing valuesMissing
carpet_area has 1836 (48.6%) missing valuesMissing
facing has 1092 (28.9%) missing valuesMissing
furnish_type has 159 (4.2%) missing valuesMissing
luxury_score has 159 (4.2%) missing valuesMissing
builtup is highly skewed (γ1 = 40.93184727)Skewed
carpet_area is highly skewed (γ1 = 24.75825056)Skewed
floorNum has 131 (3.5%) zerosZeros
luxury_score has 426 (11.3%) zerosZeros

Reproduction

Analysis started2025-10-04 05:56:11.340907
Analysis finished2025-10-04 05:56:27.649269
Duration16.31 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

property_type
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size196.3 KiB
flat
2933 
house
841 

Length

Max length5
Median length4
Mean length4.2228405
Min length4

Characters and Unicode

Total characters15937
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowflat
2nd rowflat
3rd rowflat
4th rowflat
5th rowflat

Common Values

ValueCountFrequency (%)
flat2933
77.7%
house841
 
22.3%

Length

2025-10-04T11:26:27.844357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-04T11:26:27.999581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
flat2933
77.7%
house841
 
22.3%

Most occurring characters

ValueCountFrequency (%)
f2933
18.4%
l2933
18.4%
a2933
18.4%
t2933
18.4%
h841
 
5.3%
o841
 
5.3%
u841
 
5.3%
s841
 
5.3%
e841
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter15937
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f2933
18.4%
l2933
18.4%
a2933
18.4%
t2933
18.4%
h841
 
5.3%
o841
 
5.3%
u841
 
5.3%
s841
 
5.3%
e841
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Latin15937
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f2933
18.4%
l2933
18.4%
a2933
18.4%
t2933
18.4%
h841
 
5.3%
o841
 
5.3%
u841
 
5.3%
s841
 
5.3%
e841
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII15937
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f2933
18.4%
l2933
18.4%
a2933
18.4%
t2933
18.4%
h841
 
5.3%
o841
 
5.3%
u841
 
5.3%
s841
 
5.3%
e841
 
5.3%

society
Text

Distinct671
Distinct (%)17.8%
Missing1
Missing (%)< 0.1%
Memory size243.1 KiB
2025-10-04T11:26:28.589477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length49
Median length39
Mean length16.922608
Min length1

Characters and Unicode

Total characters63849
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique290 ?
Unique (%)7.7%

Sample

1st rowmaa bhagwati residency
2nd rowapna enclave
3rd rowtulsiani easy in homes
4th rowsmart world orchard
5th rowparkwood westend
ValueCountFrequency (%)
independent484
 
4.9%
the362
 
3.6%
dlf224
 
2.2%
park219
 
2.2%
city167
 
1.7%
global163
 
1.6%
signature159
 
1.6%
m3m156
 
1.6%
emaar155
 
1.6%
heights139
 
1.4%
Other values (782)7729
77.6%
2025-10-04T11:26:29.399577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e6881
 
10.8%
6186
 
9.7%
a6045
 
9.5%
r4322
 
6.8%
n4229
 
6.6%
i3935
 
6.2%
t3816
 
6.0%
s3611
 
5.7%
l3050
 
4.8%
o2846
 
4.5%
Other values (31)18928
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter57100
89.4%
Space Separator6186
 
9.7%
Decimal Number545
 
0.9%
Other Punctuation10
 
< 0.1%
Dash Punctuation8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e6881
12.1%
a6045
 
10.6%
r4322
 
7.6%
n4229
 
7.4%
i3935
 
6.9%
t3816
 
6.7%
s3611
 
6.3%
l3050
 
5.3%
o2846
 
5.0%
d2530
 
4.4%
Other values (16)15835
27.7%
Decimal Number
ValueCountFrequency (%)
3214
39.3%
282
 
15.0%
175
 
13.8%
661
 
11.2%
835
 
6.4%
419
 
3.5%
517
 
3.1%
915
 
2.8%
714
 
2.6%
013
 
2.4%
Other Punctuation
ValueCountFrequency (%)
,7
70.0%
/2
 
20.0%
.1
 
10.0%
Space Separator
ValueCountFrequency (%)
6186
100.0%
Dash Punctuation
ValueCountFrequency (%)
-8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin57100
89.4%
Common6749
 
10.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e6881
12.1%
a6045
 
10.6%
r4322
 
7.6%
n4229
 
7.4%
i3935
 
6.9%
t3816
 
6.7%
s3611
 
6.3%
l3050
 
5.3%
o2846
 
5.0%
d2530
 
4.4%
Other values (16)15835
27.7%
Common
ValueCountFrequency (%)
6186
91.7%
3214
 
3.2%
282
 
1.2%
175
 
1.1%
661
 
0.9%
835
 
0.5%
419
 
0.3%
517
 
0.3%
915
 
0.2%
714
 
0.2%
Other values (5)31
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII63849
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e6881
 
10.8%
6186
 
9.7%
a6045
 
9.5%
r4322
 
6.8%
n4229
 
6.6%
i3935
 
6.2%
t3816
 
6.0%
s3611
 
5.7%
l3050
 
4.8%
o2846
 
4.5%
Other values (31)18928
29.6%

sector
Text

Distinct141
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size217.9 KiB
2025-10-04T11:26:29.859315image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length26
Median length10
Mean length10.08903
Min length4

Characters and Unicode

Total characters38076
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsector 7
2nd rowsector 3
3rd rowsohna road
4th rowsector 61
5th rowsector 92
ValueCountFrequency (%)
sector3536
46.7%
road187
 
2.5%
sohna175
 
2.3%
102113
 
1.5%
85110
 
1.5%
92104
 
1.4%
6994
 
1.2%
6590
 
1.2%
8189
 
1.2%
9089
 
1.2%
Other values (106)2992
39.5%
2025-10-04T11:26:30.488852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6700
17.6%
o3909
10.3%
s3791
10.0%
r3790
10.0%
e3636
9.5%
c3589
9.4%
t3547
9.3%
11084
 
2.8%
0817
 
2.1%
8805
 
2.1%
Other values (21)6408
16.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter23934
62.9%
Decimal Number7442
 
19.5%
Space Separator6700
 
17.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o3909
16.3%
s3791
15.8%
r3790
15.8%
e3636
15.2%
c3589
15.0%
t3547
14.8%
a729
 
3.0%
d263
 
1.1%
n239
 
1.0%
h213
 
0.9%
Other values (10)228
 
1.0%
Decimal Number
ValueCountFrequency (%)
11084
14.6%
0817
11.0%
8805
10.8%
9801
10.8%
6750
10.1%
7704
9.5%
2695
9.3%
3689
9.3%
5604
8.1%
4493
6.6%
Space Separator
ValueCountFrequency (%)
6700
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin23934
62.9%
Common14142
37.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o3909
16.3%
s3791
15.8%
r3790
15.8%
e3636
15.2%
c3589
15.0%
t3547
14.8%
a729
 
3.0%
d263
 
1.1%
n239
 
1.0%
h213
 
0.9%
Other values (10)228
 
1.0%
Common
ValueCountFrequency (%)
6700
47.4%
11084
 
7.7%
0817
 
5.8%
8805
 
5.7%
9801
 
5.7%
6750
 
5.3%
7704
 
5.0%
2695
 
4.9%
3689
 
4.9%
5604
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII38076
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6700
17.6%
o3909
10.3%
s3791
10.0%
r3790
10.0%
e3636
9.5%
c3589
9.4%
t3547
9.3%
11084
 
2.8%
0817
 
2.1%
8805
 
2.1%
Other values (21)6408
16.8%

price_in_lacs
Real number (ℝ)

High correlation 

Distinct552
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250.12568
Minimum7.5
Maximum3150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.6 KiB
2025-10-04T11:26:30.688834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum7.5
5-th percentile37
Q194
median150
Q3270
95-th percentile848.35
Maximum3150
Range3142.5
Interquartile range (IQR)176

Descriptive statistics

Standard deviation294.1614
Coefficient of variation (CV)1.1760544
Kurtosis15.420423
Mean250.12568
Median Absolute Deviation (MAD)71
Skewness3.3229473
Sum943974.32
Variance86530.927
MonotonicityNot monotonic
2025-10-04T11:26:30.879158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12583
 
2.2%
12066
 
1.7%
11066
 
1.7%
15065
 
1.7%
9065
 
1.7%
14062
 
1.6%
13060
 
1.6%
20056
 
1.5%
9556
 
1.5%
10051
 
1.4%
Other values (542)3144
83.3%
ValueCountFrequency (%)
7.51
 
< 0.1%
161
 
< 0.1%
17.51
 
< 0.1%
191
 
< 0.1%
208
0.2%
20.51
 
< 0.1%
216
0.2%
229
0.2%
23.51
 
< 0.1%
246
0.2%
ValueCountFrequency (%)
31501
 
< 0.1%
27501
 
< 0.1%
26002
0.1%
25001
 
< 0.1%
24001
 
< 0.1%
23001
 
< 0.1%
22001
 
< 0.1%
20003
0.1%
19502
0.1%
19003
0.1%

price_per_sqft_inrs
Real number (ℝ)

High correlation 

Distinct2649
Distinct (%)70.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13711.496
Minimum4
Maximum600000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.6 KiB
2025-10-04T11:26:31.084091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4716.65
Q16806.5
median9000
Q313763.25
95-th percentile33203.15
Maximum600000
Range599996
Interquartile range (IQR)6956.75

Descriptive statistics

Standard deviation22682.341
Coefficient of variation (CV)1.6542572
Kurtosis198.10198
Mean13711.496
Median Absolute Deviation (MAD)2757.5
Skewness11.761087
Sum51747185
Variance5.1448859 × 108
MonotonicityNot monotonic
2025-10-04T11:26:31.288970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000028
 
0.7%
800019
 
0.5%
1250017
 
0.5%
500017
 
0.5%
750014
 
0.4%
1111114
 
0.4%
666614
 
0.4%
833313
 
0.3%
2222213
 
0.3%
3333311
 
0.3%
Other values (2639)3614
95.8%
ValueCountFrequency (%)
41
< 0.1%
51
< 0.1%
71
< 0.1%
91
< 0.1%
531
< 0.1%
571
< 0.1%
582
0.1%
601
< 0.1%
611
< 0.1%
791
< 0.1%
ValueCountFrequency (%)
6000001
< 0.1%
4000001
< 0.1%
3157891
< 0.1%
3083331
< 0.1%
2909481
< 0.1%
2833331
< 0.1%
2666661
< 0.1%
2611941
< 0.1%
2453981
< 0.1%
2416661
< 0.1%

area
Text

Distinct2554
Distinct (%)67.7%
Missing0
Missing (%)0.0%
Memory size217.5 KiB
2025-10-04T11:26:31.939411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length25
Median length7
Mean length9.9809221
Min length4

Characters and Unicode

Total characters37668
Distinct characters31
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2022 ?
Unique (%)53.6%

Sample

1st row900.0
2nd row650.03
3rd row595.06
4th row1200.0
5th row1345.12
ValueCountFrequency (%)
area841
 
13.4%
sq.m841
 
13.4%
plot666
 
10.6%
built-up134
 
2.1%
30145
 
0.7%
carpet41
 
0.7%
2000.033
 
0.5%
25132
 
0.5%
8430
 
0.5%
16726
 
0.4%
Other values (2466)3608
57.3%
2025-10-04T11:26:32.749400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
.4615
 
12.3%
03641
 
9.7%
13638
 
9.7%
2523
 
6.7%
22319
 
6.2%
51871
 
5.0%
31481
 
3.9%
41353
 
3.6%
91265
 
3.4%
61248
 
3.3%
Other values (21)13714
36.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number18979
50.4%
Lowercase Letter8053
21.4%
Other Punctuation4615
 
12.3%
Space Separator2523
 
6.7%
Uppercase Letter1682
 
4.5%
Open Punctuation841
 
2.2%
Close Punctuation841
 
2.2%
Dash Punctuation134
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r882
11.0%
a882
11.0%
e882
11.0%
t841
10.4%
m841
10.4%
q841
10.4%
s841
10.4%
l800
9.9%
o666
8.3%
u268
 
3.3%
Other values (2)309
 
3.8%
Decimal Number
ValueCountFrequency (%)
03641
19.2%
13638
19.2%
22319
12.2%
51871
9.9%
31481
7.8%
41353
 
7.1%
91265
 
6.7%
61248
 
6.6%
81084
 
5.7%
71079
 
5.7%
Uppercase Letter
ValueCountFrequency (%)
A841
50.0%
P666
39.6%
B134
 
8.0%
C41
 
2.4%
Other Punctuation
ValueCountFrequency (%)
.4615
100.0%
Space Separator
ValueCountFrequency (%)
2523
100.0%
Open Punctuation
ValueCountFrequency (%)
(841
100.0%
Close Punctuation
ValueCountFrequency (%)
)841
100.0%
Dash Punctuation
ValueCountFrequency (%)
-134
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common27933
74.2%
Latin9735
 
25.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r882
9.1%
a882
9.1%
e882
9.1%
A841
8.6%
t841
8.6%
m841
8.6%
q841
8.6%
s841
8.6%
l800
8.2%
o666
6.8%
Other values (6)1418
14.6%
Common
ValueCountFrequency (%)
.4615
16.5%
03641
13.0%
13638
13.0%
2523
9.0%
22319
8.3%
51871
6.7%
31481
 
5.3%
41353
 
4.8%
91265
 
4.5%
61248
 
4.5%
Other values (5)3979
14.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII37668
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.4615
 
12.3%
03641
 
9.7%
13638
 
9.7%
2523
 
6.7%
22319
 
6.2%
51871
 
5.0%
31481
 
3.9%
41353
 
3.6%
91265
 
3.4%
61248
 
3.3%
Other values (21)13714
36.4%
Distinct2347
Distinct (%)62.2%
Missing0
Missing (%)0.0%
Memory size379.6 KiB
2025-10-04T11:26:33.373650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length124
Median length119
Mean length53.970323
Min length12

Characters and Unicode

Total characters203684
Distinct characters35
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1777 ?
Unique (%)47.1%

Sample

1st rowCarpet area: 900 (83.61 sq.m.)
2nd rowCarpet area: 650 (60.39 sq.m.)
3rd rowCarpet area: 595 (55.28 sq.m.)
4th rowCarpet area: 1200 (111.48 sq.m.)
5th rowSuper Built up area 1345(124.95 sq.m.)
ValueCountFrequency (%)
area5694
18.5%
sq.m3750
12.2%
up3090
 
10.0%
built2384
 
7.7%
super1913
 
6.2%
sq.ft1779
 
5.8%
sq.m.)carpet1206
 
3.9%
carpet728
 
2.4%
sq.m.)built704
 
2.3%
plot666
 
2.2%
Other values (2839)8910
28.9%
2025-10-04T11:26:34.234990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
27050
 
13.3%
.20799
 
10.2%
a13457
 
6.6%
r9676
 
4.8%
e9545
 
4.7%
19433
 
4.6%
s7703
 
3.8%
q7572
 
3.7%
t7473
 
3.7%
p6941
 
3.4%
Other values (25)84035
41.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter84498
41.5%
Decimal Number48134
23.6%
Space Separator27050
 
13.3%
Other Punctuation23914
 
11.7%
Uppercase Letter8784
 
4.3%
Close Punctuation5652
 
2.8%
Open Punctuation5652
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a13457
15.9%
r9676
11.5%
e9545
11.3%
s7703
9.1%
q7572
9.0%
t7473
8.8%
p6941
8.2%
u6916
8.2%
m5662
6.7%
l3756
 
4.4%
Other values (5)5797
6.9%
Decimal Number
ValueCountFrequency (%)
19433
19.6%
06729
14.0%
25812
12.1%
54828
10.0%
34047
8.4%
43774
7.8%
63750
 
7.8%
73323
 
6.9%
83227
 
6.7%
93211
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
B3090
35.2%
C1938
22.1%
S1913
21.8%
U1177
 
13.4%
P666
 
7.6%
Other Punctuation
ValueCountFrequency (%)
.20799
87.0%
:3115
 
13.0%
Space Separator
ValueCountFrequency (%)
27050
100.0%
Close Punctuation
ValueCountFrequency (%)
)5652
100.0%
Open Punctuation
ValueCountFrequency (%)
(5652
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common110402
54.2%
Latin93282
45.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a13457
14.4%
r9676
10.4%
e9545
10.2%
s7703
8.3%
q7572
8.1%
t7473
8.0%
p6941
7.4%
u6916
7.4%
m5662
 
6.1%
l3756
 
4.0%
Other values (10)14581
15.6%
Common
ValueCountFrequency (%)
27050
24.5%
.20799
18.8%
19433
 
8.5%
06729
 
6.1%
25812
 
5.3%
)5652
 
5.1%
(5652
 
5.1%
54828
 
4.4%
34047
 
3.7%
43774
 
3.4%
Other values (5)16626
15.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII203684
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
27050
 
13.3%
.20799
 
10.2%
a13457
 
6.6%
r9676
 
4.8%
e9545
 
4.7%
19433
 
4.6%
s7703
 
3.8%
q7572
 
3.7%
t7473
 
3.7%
p6941
 
3.4%
Other values (25)84035
41.3%

super_builtup
Real number (ℝ)

High correlation  Missing 

Distinct586
Distinct (%)30.6%
Missing1861
Missing (%)49.3%
Infinite0
Infinite (%)0.0%
Mean1921.8437
Minimum89
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.6 KiB
2025-10-04T11:26:34.589147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile760.2
Q11457
median1828
Q32215
95-th percentile3187.8
Maximum10000
Range9911
Interquartile range (IQR)758

Descriptive statistics

Standard deviation767.24362
Coefficient of variation (CV)0.39922269
Kurtosis10.088504
Mean1921.8437
Median Absolute Deviation (MAD)372
Skewness1.8243684
Sum3676487
Variance588662.77
MonotonicityNot monotonic
2025-10-04T11:26:34.790747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
165038
 
1.0%
195038
 
1.0%
200026
 
0.7%
157825
 
0.7%
215023
 
0.6%
164022
 
0.6%
240820
 
0.5%
190019
 
0.5%
135019
 
0.5%
193018
 
0.5%
Other values (576)1665
44.1%
(Missing)1861
49.3%
ValueCountFrequency (%)
891
< 0.1%
1451
< 0.1%
1611
< 0.1%
2151
< 0.1%
2161
< 0.1%
3251
< 0.1%
3401
< 0.1%
3521
< 0.1%
3801
< 0.1%
4061
< 0.1%
ValueCountFrequency (%)
100001
< 0.1%
69261
< 0.1%
60001
< 0.1%
58002
0.1%
55141
< 0.1%
53502
0.1%
52002
0.1%
48901
< 0.1%
48572
0.1%
48482
0.1%

builtup
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct609
Distinct (%)35.8%
Missing2072
Missing (%)54.9%
Infinite0
Infinite (%)0.0%
Mean1847.4318
Minimum2
Maximum737147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.6 KiB
2025-10-04T11:26:34.989563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile135.15
Q1368.25
median1297.5
Q31900
95-th percentile3849.05
Maximum737147
Range737145
Interquartile range (IQR)1531.75

Descriptive statistics

Standard deviation17880.549
Coefficient of variation (CV)9.6785973
Kurtosis1684.1751
Mean1847.4318
Median Absolute Deviation (MAD)752
Skewness40.931847
Sum3144329
Variance3.1971403 × 108
MonotonicityNot monotonic
2025-10-04T11:26:35.199289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36044
 
1.2%
30034
 
0.9%
190034
 
0.9%
160026
 
0.7%
130025
 
0.7%
200025
 
0.7%
170023
 
0.6%
180022
 
0.6%
20022
 
0.6%
135021
 
0.6%
Other values (599)1426
37.8%
(Missing)2072
54.9%
ValueCountFrequency (%)
21
 
< 0.1%
141
 
< 0.1%
301
 
< 0.1%
331
 
< 0.1%
404
0.1%
506
0.2%
531
 
< 0.1%
551
 
< 0.1%
561
 
< 0.1%
571
 
< 0.1%
ValueCountFrequency (%)
7371471
 
< 0.1%
95001
 
< 0.1%
90004
0.1%
82861
 
< 0.1%
80001
 
< 0.1%
75002
 
0.1%
74501
 
< 0.1%
73311
 
< 0.1%
70009
0.2%
64001
 
< 0.1%

carpet_area
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct694
Distinct (%)35.8%
Missing1836
Missing (%)48.6%
Infinite0
Infinite (%)0.0%
Mean2487.0728
Minimum15
Maximum607936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.6 KiB
2025-10-04T11:26:35.409087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile349.7
Q1830
median1296
Q31788.75
95-th percentile2926.2
Maximum607936
Range607921
Interquartile range (IQR)958.75

Descriptive statistics

Standard deviation22409.6
Coefficient of variation (CV)9.010432
Kurtosis625.91132
Mean2487.0728
Median Absolute Deviation (MAD)474
Skewness24.758251
Sum4819947
Variance5.0219017 × 108
MonotonicityNot monotonic
2025-10-04T11:26:35.819528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140042
 
1.1%
180036
 
1.0%
160036
 
1.0%
120032
 
0.8%
150030
 
0.8%
165028
 
0.7%
135028
 
0.7%
145023
 
0.6%
130023
 
0.6%
100022
 
0.6%
Other values (684)1638
43.4%
(Missing)1836
48.6%
ValueCountFrequency (%)
151
 
< 0.1%
331
 
< 0.1%
481
 
< 0.1%
501
 
< 0.1%
591
 
< 0.1%
601
 
< 0.1%
661
 
< 0.1%
721
 
< 0.1%
763
0.1%
773
0.1%
ValueCountFrequency (%)
6079361
< 0.1%
5692431
< 0.1%
5143961
< 0.1%
645291
< 0.1%
644121
< 0.1%
581411
< 0.1%
549171
< 0.1%
488111
< 0.1%
459661
< 0.1%
344011
< 0.1%

bedRoom
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3264441
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.6 KiB
2025-10-04T11:26:35.999452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8603384
Coefficient of variation (CV)0.55925738
Kurtosis18.92078
Mean3.3264441
Median Absolute Deviation (MAD)1
Skewness3.5312179
Sum12554
Variance3.460859
MonotonicityNot monotonic
2025-10-04T11:26:36.169590image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
31544
40.9%
2987
26.2%
4672
17.8%
5201
 
5.3%
1129
 
3.4%
673
 
1.9%
940
 
1.1%
830
 
0.8%
728
 
0.7%
1227
 
0.7%
Other values (9)43
 
1.1%
ValueCountFrequency (%)
1129
 
3.4%
2987
26.2%
31544
40.9%
4672
17.8%
5201
 
5.3%
673
 
1.9%
728
 
0.7%
830
 
0.8%
940
 
1.1%
1020
 
0.5%
ValueCountFrequency (%)
211
 
< 0.1%
201
 
< 0.1%
192
 
0.1%
182
 
0.1%
1611
0.3%
141
 
< 0.1%
134
 
0.1%
1227
0.7%
111
 
< 0.1%
1020
0.5%

bathroom
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3932167
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.6 KiB
2025-10-04T11:26:36.339486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9111226
Coefficient of variation (CV)0.56321855
Kurtosis18.188684
Mean3.3932167
Median Absolute Deviation (MAD)1
Skewness3.2832514
Sum12806
Variance3.6523897
MonotonicityNot monotonic
2025-10-04T11:26:36.539348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
31110
29.4%
21099
29.1%
4835
22.1%
5292
 
7.7%
1158
 
4.2%
6120
 
3.2%
940
 
1.1%
738
 
1.0%
824
 
0.6%
1221
 
0.6%
Other values (9)37
 
1.0%
ValueCountFrequency (%)
1158
 
4.2%
21099
29.1%
31110
29.4%
4835
22.1%
5292
 
7.7%
6120
 
3.2%
738
 
1.0%
824
 
0.6%
940
 
1.1%
109
 
0.2%
ValueCountFrequency (%)
211
 
< 0.1%
203
 
0.1%
184
 
0.1%
173
 
0.1%
167
 
0.2%
142
 
0.1%
134
 
0.1%
1221
0.6%
114
 
0.1%
109
0.2%

balcony
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size185.6 KiB
3+
1189 
3
1105 
2
919 
1
374 
0
187 

Length

Max length2
Median length1
Mean length1.3150503
Min length1

Characters and Unicode

Total characters4963
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row3
4th row2
5th row3

Common Values

ValueCountFrequency (%)
3+1189
31.5%
31105
29.3%
2919
24.4%
1374
 
9.9%
0187
 
5.0%

Length

2025-10-04T11:26:36.729284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-04T11:26:36.889430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
32294
60.8%
2919
24.4%
1374
 
9.9%
0187
 
5.0%

Most occurring characters

ValueCountFrequency (%)
32294
46.2%
+1189
24.0%
2919
18.5%
1374
 
7.5%
0187
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3774
76.0%
Math Symbol1189
 
24.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
32294
60.8%
2919
24.4%
1374
 
9.9%
0187
 
5.0%
Math Symbol
ValueCountFrequency (%)
+1189
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4963
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
32294
46.2%
+1189
24.0%
2919
18.5%
1374
 
7.5%
0187
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII4963
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
32294
46.2%
+1189
24.0%
2919
18.5%
1374
 
7.5%
0187
 
3.8%

floorNum
Real number (ℝ)

Zeros 

Distinct43
Distinct (%)1.1%
Missing19
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean6.8404794
Minimum0
Maximum51
Zeros131
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size29.6 KiB
2025-10-04T11:26:37.079122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q310
95-th percentile18
Maximum51
Range51
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.0377839
Coefficient of variation (CV)0.88265508
Kurtosis4.5189165
Mean6.8404794
Median Absolute Deviation (MAD)3
Skewness1.6916391
Sum25686
Variance36.454834
MonotonicityNot monotonic
2025-10-04T11:26:37.319294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3505
13.4%
2500
13.2%
1361
 
9.6%
4323
 
8.6%
8197
 
5.2%
6186
 
4.9%
10186
 
4.9%
7182
 
4.8%
5177
 
4.7%
9170
 
4.5%
Other values (33)968
25.6%
ValueCountFrequency (%)
0131
 
3.5%
1361
9.6%
2500
13.2%
3505
13.4%
4323
8.6%
5177
 
4.7%
6186
 
4.9%
7182
 
4.8%
8197
 
5.2%
9170
 
4.5%
ValueCountFrequency (%)
511
 
< 0.1%
451
 
< 0.1%
441
 
< 0.1%
432
0.1%
402
0.1%
392
0.1%
381
 
< 0.1%
352
0.1%
342
0.1%
334
0.1%

facing
Categorical

High correlation  Missing 

Distinct8
Distinct (%)0.3%
Missing1092
Missing (%)28.9%
Memory size206.1 KiB
North-East
637 
East
636 
North
397 
West
252 
South
233 
Other values (3)
527 

Length

Max length10
Median length5
Mean length6.8389262
Min length4

Characters and Unicode

Total characters18342
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWest
2nd rowWest
3rd rowNorth-East
4th rowSouth-East
5th rowNorth-East

Common Values

ValueCountFrequency (%)
North-East637
16.9%
East636
16.9%
North397
 
10.5%
West252
 
6.7%
South233
 
6.2%
North-West199
 
5.3%
South-East172
 
4.6%
South-West156
 
4.1%
(Missing)1092
28.9%

Length

2025-10-04T11:26:37.549198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-04T11:26:37.754091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
north-east637
23.8%
east636
23.7%
north397
14.8%
west252
 
9.4%
south233
 
8.7%
north-west199
 
7.4%
south-east172
 
6.4%
south-west156
 
5.8%

Most occurring characters

ValueCountFrequency (%)
t3846
21.0%
s2052
11.2%
o1794
9.8%
h1794
9.8%
E1445
 
7.9%
a1445
 
7.9%
N1233
 
6.7%
r1233
 
6.7%
-1164
 
6.3%
W607
 
3.3%
Other values (3)1729
9.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter13332
72.7%
Uppercase Letter3846
 
21.0%
Dash Punctuation1164
 
6.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t3846
28.8%
s2052
15.4%
o1794
13.5%
h1794
13.5%
a1445
 
10.8%
r1233
 
9.2%
e607
 
4.6%
u561
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
E1445
37.6%
N1233
32.1%
W607
15.8%
S561
 
14.6%
Dash Punctuation
ValueCountFrequency (%)
-1164
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin17178
93.7%
Common1164
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t3846
22.4%
s2052
11.9%
o1794
10.4%
h1794
10.4%
E1445
 
8.4%
a1445
 
8.4%
N1233
 
7.2%
r1233
 
7.2%
W607
 
3.5%
e607
 
3.5%
Other values (2)1122
 
6.5%
Common
ValueCountFrequency (%)
-1164
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII18342
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t3846
21.0%
s2052
11.2%
o1794
9.8%
h1794
9.8%
E1445
 
7.9%
a1445
 
7.9%
N1233
 
6.7%
r1233
 
6.7%
-1164
 
6.3%
W607
 
3.3%
Other values (3)1729
9.4%

agePossession
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size230.0 KiB
Relatively New
1668 
New Property
622 
Moderately Old
569 
Undefined
327 
Old Property
307 

Length

Max length18
Median length14
Mean length13.372284
Min length9

Characters and Unicode

Total characters50467
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRelatively New
2nd rowOld Property
3rd rowNew Property
4th rowUnder Construction
5th rowUnder Construction

Common Values

ValueCountFrequency (%)
Relatively New1668
44.2%
New Property622
 
16.5%
Moderately Old569
 
15.1%
Undefined327
 
8.7%
Old Property307
 
8.1%
Under Construction281
 
7.4%

Length

2025-10-04T11:26:37.968980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-04T11:26:38.182737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
new2290
31.7%
relatively1668
23.1%
property929
12.9%
old876
 
12.1%
moderately569
 
7.9%
undefined327
 
4.5%
under281
 
3.9%
construction281
 
3.9%

Most occurring characters

ValueCountFrequency (%)
e8628
17.1%
l4781
 
9.5%
t3728
 
7.4%
3447
 
6.8%
y3166
 
6.3%
r2989
 
5.9%
d2380
 
4.7%
N2290
 
4.5%
w2290
 
4.5%
i2276
 
4.5%
Other values (15)14492
28.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter39799
78.9%
Uppercase Letter7221
 
14.3%
Space Separator3447
 
6.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e8628
21.7%
l4781
12.0%
t3728
9.4%
y3166
 
8.0%
r2989
 
7.5%
d2380
 
6.0%
w2290
 
5.8%
i2276
 
5.7%
a2237
 
5.6%
o2060
 
5.2%
Other values (7)5264
13.2%
Uppercase Letter
ValueCountFrequency (%)
N2290
31.7%
R1668
23.1%
P929
12.9%
O876
 
12.1%
U608
 
8.4%
M569
 
7.9%
C281
 
3.9%
Space Separator
ValueCountFrequency (%)
3447
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin47020
93.2%
Common3447
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e8628
18.3%
l4781
 
10.2%
t3728
 
7.9%
y3166
 
6.7%
r2989
 
6.4%
d2380
 
5.1%
N2290
 
4.9%
w2290
 
4.9%
i2276
 
4.8%
a2237
 
4.8%
Other values (14)12255
26.1%
Common
ValueCountFrequency (%)
3447
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII50467
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e8628
17.1%
l4781
 
9.5%
t3728
 
7.4%
3447
 
6.8%
y3166
 
6.3%
r2989
 
5.9%
d2380
 
4.7%
N2290
 
4.5%
w2290
 
4.5%
i2276
 
4.5%
Other values (15)14492
28.7%

furnish_type
Categorical

Missing 

Distinct3
Distinct (%)0.1%
Missing159
Missing (%)4.2%
Memory size192.4 KiB
0.0
2363 
2.0
1038 
1.0
 
214

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10845
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row2.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.02363
62.6%
2.01038
27.5%
1.0214
 
5.7%
(Missing)159
 
4.2%

Length

2025-10-04T11:26:38.388247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-04T11:26:38.549132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.02363
65.4%
2.01038
28.7%
1.0214
 
5.9%

Most occurring characters

ValueCountFrequency (%)
05978
55.1%
.3615
33.3%
21038
 
9.6%
1214
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7230
66.7%
Other Punctuation3615
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
05978
82.7%
21038
 
14.4%
1214
 
3.0%
Other Punctuation
ValueCountFrequency (%)
.3615
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10845
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
05978
55.1%
.3615
33.3%
21038
 
9.6%
1214
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10845
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
05978
55.1%
.3615
33.3%
21038
 
9.6%
1214
 
2.0%

servant_room
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size184.4 KiB
0
2428 
1
1346 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3774
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02428
64.3%
11346
35.7%

Length

2025-10-04T11:26:38.709320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-04T11:26:38.858984image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
02428
64.3%
11346
35.7%

Most occurring characters

ValueCountFrequency (%)
02428
64.3%
11346
35.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3774
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02428
64.3%
11346
35.7%

Most occurring scripts

ValueCountFrequency (%)
Common3774
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02428
64.3%
11346
35.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII3774
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02428
64.3%
11346
35.7%

study room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size184.4 KiB
0
3063 
1
711 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3774
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
03063
81.2%
1711
 
18.8%

Length

2025-10-04T11:26:39.014237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-04T11:26:39.154222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
03063
81.2%
1711
 
18.8%

Most occurring characters

ValueCountFrequency (%)
03063
81.2%
1711
 
18.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3774
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03063
81.2%
1711
 
18.8%

Most occurring scripts

ValueCountFrequency (%)
Common3774
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03063
81.2%
1711
 
18.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII3774
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03063
81.2%
1711
 
18.8%

pooja room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size184.4 KiB
0
3118 
1
656 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3774
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03118
82.6%
1656
 
17.4%

Length

2025-10-04T11:26:39.309263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-04T11:26:39.464507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
03118
82.6%
1656
 
17.4%

Most occurring characters

ValueCountFrequency (%)
03118
82.6%
1656
 
17.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3774
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03118
82.6%
1656
 
17.4%

Most occurring scripts

ValueCountFrequency (%)
Common3774
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03118
82.6%
1656
 
17.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII3774
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03118
82.6%
1656
 
17.4%

store_room
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size184.4 KiB
0
3434 
1
 
340

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3774
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03434
91.0%
1340
 
9.0%

Length

2025-10-04T11:26:39.619626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-04T11:26:39.759439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
03434
91.0%
1340
 
9.0%

Most occurring characters

ValueCountFrequency (%)
03434
91.0%
1340
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3774
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03434
91.0%
1340
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
Common3774
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03434
91.0%
1340
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3774
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03434
91.0%
1340
 
9.0%

others
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size184.4 KiB
0
3356 
1
418 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3774
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03356
88.9%
1418
 
11.1%

Length

2025-10-04T11:26:39.909373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-04T11:26:40.056699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
03356
88.9%
1418
 
11.1%

Most occurring characters

ValueCountFrequency (%)
03356
88.9%
1418
 
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3774
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03356
88.9%
1418
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common3774
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03356
88.9%
1418
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3774
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03356
88.9%
1418
 
11.1%

luxury_score
Real number (ℝ)

Missing  Zeros 

Distinct161
Distinct (%)4.5%
Missing159
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean72.33361
Minimum0
Maximum174
Zeros426
Zeros (%)11.3%
Negative0
Negative (%)0.0%
Memory size29.6 KiB
2025-10-04T11:26:40.220494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q135
median60
Q3110
95-th percentile174
Maximum174
Range174
Interquartile range (IQR)75

Descriptive statistics

Standard deviation52.786137
Coefficient of variation (CV)0.72975947
Kurtosis-0.87521384
Mean72.33361
Median Absolute Deviation (MAD)37
Skewness0.4527936
Sum261486
Variance2786.3762
MonotonicityNot monotonic
2025-10-04T11:26:40.431234image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0426
 
11.3%
49343
 
9.1%
174196
 
5.2%
4457
 
1.5%
3856
 
1.5%
16555
 
1.5%
7255
 
1.5%
6049
 
1.3%
3747
 
1.2%
4544
 
1.2%
Other values (151)2287
60.6%
(Missing)159
 
4.2%
ValueCountFrequency (%)
0426
11.3%
56
 
0.2%
66
 
0.2%
740
 
1.1%
824
 
0.6%
98
 
0.2%
127
 
0.2%
1310
 
0.3%
1411
 
0.3%
1541
 
1.1%
ValueCountFrequency (%)
174196
5.2%
1691
 
< 0.1%
1688
 
0.2%
16721
 
0.6%
16611
 
0.3%
16555
 
1.5%
1613
 
0.1%
16026
 
0.7%
15923
 
0.6%
15834
 
0.9%

Interactions

2025-10-04T11:26:24.989565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:13.474783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:14.769685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:16.619477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:17.979934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:19.429745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:20.965776image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:22.307880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:23.708925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:25.129496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:13.629824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:14.909504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:16.759227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:18.170096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:19.589460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:21.109648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:22.449604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:23.839309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:25.274380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:13.769847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:15.039565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:16.899475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:18.334549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:19.729363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:21.259549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:22.606377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:23.989029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:25.634635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:13.889882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:15.189312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:17.034751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:18.449956image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:19.879705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:21.409531image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:22.734494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:24.118860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:25.779536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:14.029471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:15.329387image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:17.179678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:18.589783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:20.114585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:21.559950image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:22.884094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:24.258947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:25.929920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:14.169740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:15.479184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:17.357081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:18.709667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:20.255858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:21.704568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:23.018948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:24.439333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:26.079722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:14.359185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:15.648956image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:17.509638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:18.859856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:20.442449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:21.850011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:23.229553image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:24.584446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:26.239443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:14.499839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:16.229536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:17.664730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:19.009866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:20.689431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:21.999758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:23.428991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:24.729299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:26.399473image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:14.629767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:16.469231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:17.829725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:19.181827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:20.819344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:22.139939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:23.563962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-04T11:26:24.849520image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-10-04T11:26:40.599563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
agePossessionbalconybathroombedRoombuiltupcarpet_areafacingfloorNumfurnish_typeluxury_scoreotherspooja roomprice_in_lacsprice_per_sqft_inrsproperty_typeservant_roomstore_roomstudy roomsuper_builtup
agePossession1.0000.2720.1100.1280.0000.0000.0920.1250.1000.1300.1110.1890.1010.0510.3730.2920.1460.1420.085
balcony0.2721.0000.2230.1730.0000.0250.0160.0790.0600.0910.0800.1920.1360.0330.2070.4370.1400.1800.304
bathroom0.1100.2231.0000.863-0.0010.6100.039-0.0040.0510.0640.0640.2830.7210.4070.4700.5190.2410.1690.821
bedRoom0.1280.1730.8631.000-0.1210.5810.028-0.0970.000-0.0150.0710.2910.6830.4120.5940.3170.2230.1490.801
builtup0.0000.000-0.001-0.1211.0000.9671.0000.3550.0950.2340.0000.0000.004-0.3970.0000.0000.0000.0000.927
carpet_area0.0000.0250.6100.5810.9671.0000.0000.1520.0000.1680.0170.0000.6210.1420.0000.0000.0000.0050.894
facing0.0920.0160.0390.0281.0000.0001.0000.0000.0430.0220.0000.0260.0220.0000.0920.0400.0380.0000.000
floorNum0.1250.079-0.004-0.0970.3550.1520.0001.0000.0290.1770.0280.0990.004-0.1190.4720.0810.1080.0750.155
furnish_type0.1000.0600.0510.0000.0950.0000.0430.0291.0000.2370.0000.0690.0620.0000.0590.1260.0590.0260.048
luxury_score0.1300.0910.064-0.0150.2340.1680.0220.1770.2371.0000.0110.0740.069-0.0450.3200.1890.0500.0000.172
others0.1110.0800.0640.0710.0000.0170.0000.0280.0000.0111.0000.0290.0340.0350.0220.0000.1020.0270.082
pooja room0.1890.1920.2830.2910.0000.0000.0260.0990.0690.0740.0291.0000.3360.0440.2520.2480.3060.3090.154
price_in_lacs0.1010.1360.7210.6830.0040.6210.0220.0040.0620.0690.0340.3361.0000.7430.5440.3700.3010.2450.775
price_per_sqft_inrs0.0510.0330.4070.412-0.3970.1420.000-0.1190.000-0.0450.0350.0440.7431.0000.2050.0460.0000.0320.288
property_type0.3730.2070.4700.5940.0000.0000.0920.4720.0590.3200.0220.2520.5440.2051.0000.0670.2410.1231.000
servant_room0.2920.4370.5190.3170.0000.0000.0400.0810.1260.1890.0000.2480.3700.0460.0671.0000.1580.1770.588
store_room0.1460.1400.2410.2230.0000.0000.0380.1080.0590.0500.1020.3060.3010.0000.2410.1581.0000.2230.043
study room0.1420.1800.1690.1490.0000.0050.0000.0750.0260.0000.0270.3090.2450.0320.1230.1770.2231.0000.116
super_builtup0.0850.3040.8210.8010.9270.8940.0000.1550.0480.1720.0820.1540.7750.2881.0000.5880.0430.1161.000

Missing values

2025-10-04T11:26:26.629959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-04T11:26:27.099376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-10-04T11:26:27.449373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

property_typesocietysectorprice_in_lacsprice_per_sqft_inrsareaareaWithTypesuper_builtupbuiltupcarpet_areabedRoombathroombalconyfloorNumfacingagePossessionfurnish_typeservant_roomstudy roompooja roomstore_roomothersluxury_score
0flatmaa bhagwati residencysector 745.05000.0900.0Carpet area: 900 (83.61 sq.m.)NaNNaN900.02214.0WestRelatively New0.00000028.0
1flatapna enclavesector 350.07692.0650.03Carpet area: 650 (60.39 sq.m.)NaNNaN650.02211.0WestOld Property2.00000037.0
2flattulsiani easy in homessohna road40.06722.0595.06Carpet area: 595 (55.28 sq.m.)NaNNaN595.022312.0NaNNew Property0.00000036.0
3flatsmart world orchardsector 61147.012250.01200.0Carpet area: 1200 (111.48 sq.m.)NaNNaN1200.02222.0NaNUnder Construction0.00100076.0
4flatparkwood westendsector 9270.05204.01345.12Super Built up area 1345(124.95 sq.m.)1345.0NaNNaN2235.0NaNUnder Construction0.0010000.0
5flatsignature global infinity mallsector 3641.06269.0654.01Built Up area: 654 (60.76 sq.m.)NaN654.0NaN2233.0NaNUndefined0.0000000.0
6flatthe cocoondwarka expressway200.013333.01500.04Super Built up area 1500(139.35 sq.m.)1500.0NaNNaN3335.0NaNNew Property0.0000000.0
7flatats triumphsector 104180.07860.02290.08Carpet area: 2290 (212.75 sq.m.)NaNNaN2290.034314.0NaNNew Property0.00000060.0
8flatvatika xpressionssector 88b110.08148.01350.02Built Up area: 1350 (125.42 sq.m.)Carpet area: 1050 sq.ft. (97.55 sq.m.)NaN1350.01050.0243+2.0North-EastUnder Construction0.00100058.0
9flatraheja revantasector 78475.016885.02813.15Built Up area: 2813 (261.34 sq.m.)NaN2813.0NaN33231.0NaNUnder Construction0.010000100.0
property_typesocietysectorprice_in_lacsprice_per_sqft_inrsareaareaWithTypesuper_builtupbuiltupcarpet_areabedRoombathroombalconyfloorNumfacingagePossessionfurnish_typeservant_roomstudy roompooja roomstore_roomothersluxury_score
3764houseindependentsector 31350.024155.0(135 sq.m.) Plot AreaPlot area 161(134.62 sq.m.)NaN161.0NaN4332.0South-WestModerately OldNaN00010NaN
3765houseindependentsector 46565.023870.0(220 sq.m.) Plot AreaPlot area 263(219.9 sq.m.)NaN263.0NaN863+3.0South-WestModerately OldNaN10000NaN
3766houseindependentsector 46355.024500.0(135 sq.m.) Plot AreaPlot area 161(134.62 sq.m.)NaN161.0NaN543+3.0North-WestModerately OldNaN10000NaN
3767houseindependentsector 46360.024845.0(135 sq.m.) Plot AreaPlot area 161(134.62 sq.m.)NaN161.0NaN553+3.0South-EastModerately OldNaN10000NaN
3768houseindependentsector 55310.020026.0(144 sq.m.) Plot AreaPlot area 172(143.81 sq.m.)NaN172.0NaN543+2.0North-EastModerately OldNaN10010NaN
3769houseindependentsector 57475.028787.0(149 sq.m.) Plot AreaPlot area 1600(148.64 sq.m.)Built Up area: 1700 sq.ft. (157.94 sq.m.)Carpet area: 1650 sq.ft. (153.29 sq.m.)NaN1700.01650.03332.0North-WestModerately OldNaN00010NaN
3770housedlf city phase 1sector 26550.030556.0(167 sq.m.) Plot AreaPlot area 200(167.23 sq.m.)NaN200.0NaN4432.0North-EastModerately OldNaN11100NaN
3771housedlf city plots phase 2sector 25425.031481.0(125 sq.m.) Plot AreaPlot area 150(125.42 sq.m.)NaN150.0NaN3232.0NorthOld PropertyNaN01000NaN
3772housedlf city phase 1sector 26450.033333.0(125 sq.m.) Plot AreaPlot area 150(125.42 sq.m.)NaN150.0NaN3322.0EastModerately OldNaN11000NaN
3773housedlf city phase 1sector 26325.033129.0(91 sq.m.) Plot AreaPlot area 109(91.14 sq.m.)NaN109.0NaN3332.0WestOld PropertyNaN01000NaN